The compaction success of vibratory roller compaction can be assessed by systems for continuous compaction control (CCC) or intelligent compaction (IC) which calculate soil stiffness-proportional quantities based on measurements of the motion behavior of the vibrating drum. However, state-of-the-art intelligent compaction meter values (ICMV) do not only depend on the stiffness of the soil but are also strongly influenced by machine and process parameters. In this paper, the methodology for determining an advanced ICMV is presented, in which the mechanical properties of the soil, the process parameters and geometric relationships in the contact area between the drum and the soil are directly included in the calculation. The methodology is explained on the example of measurement data from a compaction test conducted on sandy gravel with a heavy single-drum roller. The results of the novel ICMV are compared with those of the most widely used IC systems.

Intelligent compaction (IC) technology based on the continuous compaction control (CCC) technology enables real-time monitoring, evaluation, and feedback of compaction quality. However, the lack of a viscoelastoplastic constitutive model of hot asphalt mixture under triaxial stress states, as well as the difficulty in simulating the time-varying characteristics of material properties, hinders the research and application of IC in asphalt concrete (AC) layers. Therefore, this paper proposes a viscoelastoplastic constitutive model for AC20 asphalt mixture and establishes a vibrating compaction numerical model for time-varying characteristics in material properties using finite-element modeling (FEM) analysis software Abaqus. Additionally, the stress state of the pavement was analyzed, and adjustment methods for vibrating compaction technology are proposed to further improve the compaction quality of the AC lower surface layer. Subsequently, the intelligent compaction measurement values (ICMVs) were calculated according to the acceleration and displacement response of the roller, and the reasons for the change in ICMVs were determined. Field test results verified that the established numerical model can realistically simulate the mechanical behavior of asphalt pavement under actual vibrating compaction conditions, thus providing a theoretical basis for the application of IC feedback control technology in AC lower surface layer.

Intelligent compaction (IC) has emerged as a breakthrough technology that utilizes advanced sensing, data transmission, and control systems to optimize asphalt pavement compaction quality and efficiency. However, accurate assessment of compaction status remains challenging under real construction conditions. This paper reviewed recent progress and applications of smart sensors and machine learning (ML) to address existing limitations in IC. The principles and components of various advanced sensors deployed in IC systems were introduced, including SmartRock, fiber Bragg grating, and integrated circuit piezoelectric acceleration sensors. Case studies on utilizing these sensors for particle behavior monitoring, strain measurement, and impact data collection were reviewed. Meanwhile, common ML algorithms including regression, classification, clustering, and artificial neural networks were discussed. Practical examples of applying ML to estimate mechanical properties, evaluate overall compaction quality, and predict soil firmness through supervised and unsupervised models were examined. Results indicated smart sensors have enhanced compaction monitoring capabilities but require robustness improvements. ML provides a data-driven approach to complement traditional empirical methods but necessitates extensive field validation. Potential integration with digital construction technologies such as building information modeling and augmented reality was also explored. In conclusion, leveraging emerging sensing and artificial intelligence presents opportunities to optimize the IC process and address key challenges. However, cooperation across disciplines will be vital to test and refine technologies under real-world conditions. This study serves to advance understanding and highlight priority areas for future research toward the realization of IC's full potential.